Bayesian Analysis Made Simple

Woodward, Phil

doi:10.1201/b11235

Cited by 10 publications

(5 citation statements)

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“…To further explore the relationships suggested by analyses of the imputed dataset for time to first heavy drinking day and average drinks per drinking day (which approached significance with multivariate analysis of imputed data), we created Bayesian Simulations using the arm package in R (version 1.6) (Woodward, 2011) to virtually increase the sample size. The Bayesian analysis is based on Markov Chain Monte Carlo (MCMC) sampling, allowing us to implement an algorithm of 10,000 simulations in the models presented here.…”

Section: 0 Analytical Approachmentioning

confidence: 99%

“…The Bayesian analysis is based on Markov Chain Monte Carlo (MCMC) sampling, allowing us to implement an algorithm of 10,000 simulations in the models presented here. In all of the simulations, the first 4000 initial MCMC samples were discarded (“burn-in”) under an assumption of convergence past this point (Woodward, 2011). We used informative prior normative data based on the observed data.…”

Section: 0 Analytical Approachmentioning

confidence: 99%

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Extended-release naltrexone reduces alcohol consumption among released prisoners with HIV disease as they transition to the community

Springer

Paola

Azar

et al. 2017

Drug and Alcohol Dependence

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Background Alcohol use disorders (AUDs) are highly prevalent among persons living with HIV (PLH) within the criminal justice system (CJS). Extended-release naltrexone (XR-NTX) has not been previously evaluated among CJS-involved PLH with AUDs. Methods A randomized, double-blind, placebo-controlled trial was conducted among 100 HIV+ prisoners with AUDs. Participants were randomized 2:1 to receive 6 monthly injections of XR-NTX or placebo starting one week prior to release. Using multiple imputation strategies for data missing completely at random, data were analyzed for the 6-month post-incarceration period. Main outcomes included: time to first heavy drinking day; number of standardized drinks/drinking day; percent of heavy drinking days; pre- to post-incarceration change in average drinks/day; total number of drinking days; and a composite alcohol improvement score comprised of all 5 parameters. Results There was no statistically significant difference between treatment arms for time-to-heavy-drinking day. However participants aged 20–29 years who received XR-NTX had a longer time to first heavy drinking day compared to the placebo group (24.1 vs. 9.5 days; p <0.001). There were no statistically significant differences for other individual drinking outcomes. A sub-analysis found participants who received ≥4 XR-NTX were more likely (p<0.005) to have improved composite alcohol scores than the placebo group. Post-hoc power analysis revealed that despite the study being powered for HIV outcomes, sufficient power (.94) was available to distinguish the observed differences. Conclusions Among CJS-involved PLH with AUDs transitioning to the community, XR-NTX lengthens the time to heavy drinking day for younger persons; reduces alcohol consumption when using a composite alcohol consumption score; and is not associated with any serious adverse events.

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Section: 0 Analytical Approachmentioning

confidence: 99%

Section: 0 Analytical Approachmentioning

confidence: 99%

Extended-release naltrexone reduces alcohol consumption among released prisoners with HIV disease as they transition to the community

Springer

Paola

Azar

et al. 2017

Drug and Alcohol Dependence

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“…Prior to summarizing the posterior, we thinned chains by 25 to yield samples of 8,000 points. Thinning helped to account for any autocorrelation of MCMC generated values and facilitated the evaluation of convergence patterns (e.g., chains converging to the same distribution with the same degree of variation; see Woodward :figs. 1.8–1.12).…”

Section: Methodsmentioning

confidence: 99%

“…1.8–1.12). We considered the precision of MCMC samples to be adequate if MC errors were less than 0.05 of the posterior standard deviations of the parameters of interest (Woodward ). We provide posterior estimates of the mean, standard deviation, median, and 95% credible intervals (with limits at the 2.5% and 97.5% percentiles) of the following population and management parameters:

\overset{r}{true}

\overset{K}{true}

{\overset{h}{true}}_{msy}

{\overset{H}{true}}_{msy}

{\overset{N}{true}}_{msy}

{\overset{N}{true}}_{2014}

, and

{\overset{σ}{true}}_{process}^{2}

.…”

Section: Methodsmentioning

confidence: 99%

“…Because measures of variation were not available for total harvest estimates derived from these sources of hunting data, we acknowledge that model-based estimates of population and management parameters may seem reasonable but associated intervals may be too narrow. Harvest regulations did not change for the scaly-naped pigeon in 1986-2012, but the aggregate daily bag limit of the 3 dove game species was increased to 20 individuals in 2008-2012(USDOI 2012. Additionally, the hunting season was suspended after hurricanes in 1989 and 1998.…”

Section: Bayesian State-space Modelmentioning

confidence: 99%

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Monitoring and modeling of population dynamics for the harvest management of scaly-naped pigeons in Puerto Rico

Rivera-Milán

Boomer

Martínez³

2014

Jour. Wild. Mgmt.

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The scaly‐naped pigeon (Patagioenas squamosa) is threatened by hunting in the Caribbean. At present, the pigeon is abundant in Puerto Rico, but overharvesting is a major concern; therefore, the development of a sustainable harvest strategy is a management priority. The management objective of the harvest strategy is to maximize hunting opportunity while keeping the population above an abundance threshold (NT) of 260,000 pigeons. To facilitate operational development and implementation of the harvest strategy, we conducted point‐transect distance sampling to estimate population size, and mail and telephone hunter surveys to estimate total harvest. We used monitoring data and a Bayesian state‐space model to estimate population and harvest management parameters, and predicted changes in population size as a function of expected total harvest. Population size averaged 262,899 pigeons (SE = 122,087) in April–June 1986–2012, and total harvest averaged 40,760 pigeons (SD = 43,405) in September–November 1986–2011. Intrinsic rate of growth was 0.442 (SD = 0.142), carrying capacity was 524,900 pigeons (SD = 119,200), maximum sustainable harvest rate was 0.221 (SD = 0.071) with a total harvest of 57,988 pigeons (SD = 14,640), and equilibrium population size was 262,500 pigeons (SD = 59,620). Because the population recently recovered from deforestation and has been affected by hurricanes, a conservative harvest level was prescribed at 50,311 pigeons (SD = 24,939). However, harvest in 2008–2011 was 2.1 times larger than the maximum sustainable yield. Assuming 122,905 pigeons were harvested per year in 2012 and 2013, we predicted a population size of 216,000 pigeons (SD = 90,770) in 2014, suggesting that restrictive regulations may need to be prescribed to meet the management objective. Our monitoring and modeling framework is an important first step in the development and implementation of a sustainable harvest strategy for the scaly‐naped pigeon, and the approach can be applied to the management of other columbids in Puerto Rico. © 2014 The Wildlife Society.

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Spatiotemporal variation in mechanisms driving regional‐scale population dynamics of a Threatened grassland bird

Ethier

Koper

Nudds

2017

Ecology and Evolution

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To achieve national population targets for migratory birds, landscape‐level conservation approaches are increasingly encouraged. However, knowledge of the mechanisms that drive spatiotemporal patterns in population dynamics are needed to inform scale‐variant policy development. Using hierarchical Bayesian models and variable selection, we determined by which mechanism(s), and to what extent, changes in quantity and quality of surrogate grassland habitats contributed to regional variation in population trends of an obligatory grassland bird, Bobolink (Dolichonyx oryzivorous). We used North American Breeding Bird Survey data to develop spatially explicit models of regional population trends over 25 years across 35 agricultural census divisions in Ontario, Canada. We measured the strength of evidence for effects of land‐use change on population trends over the entire study period and over five subperiods. Over the entire study period, one region (Perth) displayed strong evidence of population decline (95% CI is entirely below 0); four regions displayed strong evidence of population increase (Bruce, Simcoe, Peterborough, and Northumberland). Population trends shifted spatially among subperiods, with more extreme declines later in time (1986–1990: 28% of 35 census divisions, 1991–1995: 46%, 1996–2000: 40%, 2001–2005: 66%, 2006–2010: 82%). Important predictors of spatial patterns in Bobolink population trends over the entire study period were human development and fragmentation. However, factors inferred to drive patterns in population trends were not consistent over space and time. This result underscores that effective threat identification (both spatially and temporally) and implementation of flexible, regionally tailored policies will be critical to realize efficient conservation of Bobolink and similar at‐risk species.

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Bayesian Analysis Made Simple

Cited by 10 publications

References 0 publications

Extended-release naltrexone reduces alcohol consumption among released prisoners with HIV disease as they transition to the community

Extended-release naltrexone reduces alcohol consumption among released prisoners with HIV disease as they transition to the community

Monitoring and modeling of population dynamics for the harvest management of scaly-naped pigeons in Puerto Rico

Spatiotemporal variation in mechanisms driving regional‐scale population dynamics of a Threatened grassland bird

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